System and method for sample detection based on low-frequency spectral components

ABSTRACT

Method and apparatus for detecting a selected material in a sample are disclosed. In the method, the sample is placed adjacent a detector coil, for generating an electromagnetic time-domain signal composed of sample source radiation. The signal is first conditioned to convert the signal to an amplified conditioned signal from which frequency components above a selected frequency have been removed, then filtered to selectively pass low-frequency spectral components that are (i) in a frequency range between dc and 50 khz, and (ii) characteristic of the selected material. The filtered signal is cross-correlated with a data set of low-frequency spectral components that are (i) in a frequency range between dc and 50 khz, and (ii) characteristic of a selected material, to produce a frequency-domain spectrum in the frequency range within DC to 50 khz. This spectrum is then used to determine whether the frequency-domain spectrum contains one or more low-frequency signal components that are characteristic of the selected material, and diagnostic of the presence or absence of such material in the sample.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.10/805,066, filed Mar. 19, 2004 now U.S. Pat. No. 6,952,652, which is acontinuation of International Application No. PCT/US2003/11834 filedApr. 18, 2003 and claims priority to U.S. Provisional Patent ApplicationNos. 60/374,941, filed Apr. 19, 2002; 60/374,043, filed Apr. 19, 2002;and 60/433,361, filed Dec. 12, 2002, all of which are incorporatedherein in their entireties by reference.

REFERENCES

U.S. Pat. Nos. 4,751,515, 5,734,353, 5,952,978, 5,959,548, 6,028,558,6,204,821, 6,323,632.

-   -   Introduction to Digital Signal Processing, Proakis and        Manolakis, (Macmillan, 1988, ISBN 0-02-396810-9).    -   Advanced Digital Signal Processing, Proakis, Rader, Ling and        Nikias, (Macmillan, 1992, ISBN 0-02-396841-9).    -   The Fast Fourier Transform and Its Applications, Brigham, E.        Oren, (Prentice-Hall, Inc., 1988).    -   The Fourier Transform and Its Applications, Bracewell, Ron N.,        (McGraw-Hill Book Company, 1965).    -   The analysis and restoration of astronomical data via the fast        Fourier transform, Brault, J. W and White, O. R., 1971,        Astronomy & Astrophysics., 13, pp. 169-189.    -   Split-radix FFT Algorithm, Duhamel, P. and Hollmann, H., 1984,        Electr. Letters, vol. 1, pp. 14-16, January.    -   An algorithm for the machine calculation of complex Fourier        series, Cooley, J. W and Tukey, J. W., 1965, Mathematics of        Computation, 19, 90, pp. 297-301.    -   Digital Signal Processing, Oppenheim, Alan & Schafer, Ronald,        (1975, ISBN 0-13-214635-5).    -   An Introduction to Fourier Theory, by Forrest Hoffman,

(“http://aurora.phys.utk.edu/˜forrest/papers/fourier/index.html#introduction”).

BACKGROUND

There are a variety of spectroscopic tools for characterizing atomic ormolecular compound. These include, but are not limited to, x-ray, UV,visible-light, infrared and microwave spectroscopy, and nuclear andelectron spin resonance (NMR and ESR) spectroscopy. In general,spectroscopic tools are useful for at least four different type ofchemical-analytical problems: first, to characterize an atomic andmolecular compound according to its spectrographic features, e.g.,spectral components; second, to determine the atomic composition of acompound, according to the spectral characteristics of atoms making upthe compound; third, to determine 2-D or 3-D conformation of a molecularcompound according to the spectral characteristic of atom-atominteractions in the compound; and fourth, to detect and identifycomponents, e.g., contaminants, in a sample according to thedistinguishing spectral characteristics of the compound being detected.

Most existing spectroscopic tools provide some unique advantage(s) interms of sensitivity, the information gained, ease of measurement andcost. Because each tool provides information not otherwise available, itis generally advantageous to be able to bring to bear on anychemical-analytical, as many pertinent spectroscopic tools as possible.

SUMMARY

In one aspect, the invention includes an apparatus for use in detectinga selected material in a sample or environment(s). A data storage devicein the apparatus stores, for each of one or more preselected materialsincluding the selected material, a data set containing low-frequencyspectral components that are (i) in a selected frequency range betweendc to 50 Khz, and (ii) characteristic of that material. A detectorassembly in the apparatus has a detector coil for generating atime-domain signal having signal components related to low-frequencyelectromagnetic radiation produced by the selected material in thesample, when the sample is placed adjacent the coil. Signal conditioningcomponents in the apparatus convert the signal from the detector coil toan amplified conditioned signal from which frequency components above aselected frequency have been removed.

An electronic computer in the apparatus receives the conditioned signaland processes this signal by the steps of

(i) retrieving from the data storage device (a), a data set oflow-frequency spectral components characteristic of the selected samplematerial,

(ii) filtering the conditioned signal, with such in digitized form, toselectively pass low-frequency spectral components corresponding tothose of the retrieved data set;

(iii) cross-correlating the filtered signal from (ii) with the data setof low-frequency spectral components from (i) to produce afrequency-domain spectrum in a frequency range within dc to 50 khz, and

(iv) determining whether the frequency-domain spectrum contains one ormore low-frequency signal components that are characteristic of theselected material, and diagnostic of the presence or absence of suchmaterial in the sample.

The output of the processing steps may be stored or displayed on aninterface device connected to the computer.

For use in detecting a material in a fluid sample, the detector assemblymay include a sample tube having sample inlet and outlet ports throughwhich sample can be directed through the tube. The detector coil may bewound about the tube in a winding direction substantially perpendicularto the direction of sample flow in the tube. The tube is preferablyformed of pyrex glass or other material that is transparent tolow-frequency electromagnetic signals, but itself produces little or nolow-frequency signal. The detector assembly may further include atoroidal ferrite core having the collector tube disposed about at leasta portion of the circumference of the core, with the detector coil woundaround the tube and core in a radial winding direction. Also in thisgeneral embodiment, the detector assembly may further include a sourceof Gaussian noise and a noise-injection coil wound about thecircumference of the toroidal core, through which Gaussian noise can beintroduced from the source into the sample in the tube.

In another general embodiment, the detector coil in the detectorassembly includes a Helmholz coil having a pair of opposed coil elementsbetween which the sample can be placed. In one embodiment, the opposedcoil elements define an open sample-detection region therebetween,through which self-supporting samples can be inserted and removed.

In still another embodiment, the detector coil is a Tesla coil.

For use in detecting gaseous or particulate material in a gaseous-streamsample, the detector assembly includes a filter effective to trap samplematerial, as the sample passes through the filter. The detector coil isplaced against the filter with its winding direction substantiallyparallel to the filter.

The computer may be operable, in carrying out processing step (iv), toidentify the frequencies of low-frequency signal components in thespectrum whose cross-spectral correlations have a selected statisticalmeasure above background spectral noise.

The computer may be operable, in carrying out step (iv), to (iva)receive an additional frequency-domain spectrum for a given sample,(ivb) add the additional spectrum to the originally produced spectrum,and average the added spectra, and (ivc) repeat steps (iva) and (ivb)until components in the summed and averaged spectrum have a selectedstatistical measure above background noise.

In another aspect, the invention includes a method for detecting aselected material in a sample. In practicing the method, a sample isplaced adjacent a detector coil, thereby to generate an electromagnetictime-domain signal composed of sample source radiation. The signal isconverted to an amplified conditioned signal from which frequencycomponents above a selected frequency have been removed, then filteredto selectively pass low-frequency spectral components that are (i) in afrequency range between dc and 50 kHz, and (ii) characteristic of theselected material.

The filtered signal is cross-correlated with a data set of low-frequencyspectral components that are (i) in a frequency range between dc and 50kHz, and (ii) characteristic of a selected material, to produce afrequency-domain spectrum in the frequency range within DC to 50 kHz. Bydetermining whether the frequency-domain spectrum contains one or morelow-frequency signal components that are characteristic of the selectedmaterial, the presence or absence of such material in the sample isdetected.

For use in detecting a material in a fluid sample, the sample may beflowed through a sample tube having sample inlet and outlet ports. Herethe detector coil may be wound about the tube in a winding directionsubstantially perpendicular to the direction of sample flow in the tube.The sample tube may be disposed adjacent a toroidal ferrite core, withthe detector coil wound around the tube and core in a radial windingdirection. In this embodiment, the method may further include injectingGaussian noise into the sample during generation of the time-domainsignal.

Alternatively, the detector coil may be a Helmholz coil having a pair ofopposed coil elements. In this embodiment, the sample may be placedbetween the coil elements.

For use in detecting gaseous or particulate material in a gaseous-streamsample, the sample may be passed sample through a filter effective totrap sample material, as the sample passes through the filter. Thedetector coil is placed adjacent the filter, preferably with a windingdirection substantially parallel to the plane of the filter.

The step of determining whether the frequency-domain spectrum containsone or more low-frequency signal components that are characteristic ofthe selected material may include identifying the frequencies oflow-frequency signal components in the spectrum whose cross-spectralcorrelations have a selected statistical measure above backgroundspectral noise.

The step of determining whether the frequency-domain spectrum containsone or more low-frequency signal components that are characteristic ofthe selected material may include (a) receiving an additionalfrequency-domain spectrum for a given sample, (b) adding the additionalspectrum to the originally produced spectrum, and averaging the addedspectra, and (c) repeating steps (a) and (b) until components in thesummed and averaged spectrum have a selected statistical measure abovebackground noise.

These and other aspects and features of the invention will become morefully apparent when the following detailed description of embodimentsthe invention is read in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows components of an apparatus constructed in accordance withone embodiment of the invention;

FIG. 2 shows components of an apparatus constructed in accordance withanother embodiment of the invention;

FIG. 3 is a block diagram of signal processing for streaming data

FIG. 4 shows elements of a toroidal detector assembly constructed inaccordance with one embodiment of the invention;

FIG. 5 shows elements of a collector assembly for air-borne materialconstructed in accordance with another embodiment of the invention;

FIG. 6A shows a time-domain signal recorded from an NaCl solution, inaccordance the method of the invention;

FIG. 6B shows the time-domain signal from FIG. 6A after signalamplification and removal of higher-frequency components;

FIG. 6C shows the time-domain signal from FIG. 6B after notch filteringto pass selected low-frequency spectral components of a solution ofNaCl; and

FIG. 6D shows a frequency-domain spectrum of NaCl produced bycross-correlating the filtered signal from 6C with a data set oflow-frequency spectral components associated with NaCl; showing alow-frequency spectral component characteristic of NaCl.

FIG. 7 is a diagram of the processing unit included in the detectionsystem.

FIG. 8 is a diagram of an alternative processing unit of that of FIG. 7.

FIG. 9 is a flow diagram of the signal detection and processingperformed by the present system.

DETAILED DESCRIPTION

I. Definitions

The terms below have the following definitions unless indicatedotherwise.

“Sample that exhibits molecular rotation” refers to a sample material,which may be in gaseous, liquid or solid form (other than a solid metal)in which one or more of the molecular compounds or atomic ions making upor present in the sample exhibit rotation.

“Time-domain signal” or “time-series signal” refers to a signal withtransient signal properties that change over time.

“Sample-source radiation” refers to magnetic flux emissions resultingform the rotation of a molecular dipole in a magnetic field.

“Gaussian noise” means random noise having a Gaussian powerdistribution.

“Stationary white Gaussian noise” means random Gaussian noise that hasno predictable components.

“Frequency-domain spectrum” refers to a Fourier frequency plot of atime-domain signal.

“Spectral components” refer to singular or repeating qualities within atime-domain signal that can be measured in the frequency, amplitude,and/or phase domains. Spectral components will typically refer tosignals present in the frequency domain.

“Similar sample,” with reference to a first sample, refers to the samesample or a sample having substantially the same sample components asthe first sample.

II. Apparatus

The apparatus of the invention operates at the extreme low end of theelectromagnetic spectrum between Direct Current (DC) and 50 Kilohertz.(KhZ) This technology is passive, which means it does not require‘painting’ the target with harmful, or ionizing radiation (although someembodiments inject white noise into the system/sample). In at least oneembodiment, this molecular sensing invention detects naturally occurringthermal electromagnetic radiation and offers a fast, simple method forremotely sensing extremely small molecular concentrations. Applicationsinclude, but are not limited to: remote sensing of weapons, explosivesand biohazards, detection of genetically modified grains and crops,real-time detection of organisms such as E. coli and the aids virus, andreal-time chemical analysis of process streams in volatile, corrosive orthermal environments.

This remote sensing system detects molecular electromagnetic emissions,performs time series Fourier spectral analysis and compares the resultsagainst archived data, identifying molecular materials in near real timethat may not be otherwise detectable. The device has the ability todetect multiple materials simultaneously and is scalable from smallhandheld devices to large industrial applications.

FIG. 1 illustrates an apparatus 20 constructed according to oneembodiment of the invention, for detecting a selected material, e.g.,compound, ionic species, particulate material, contained in a sample.The apparatus include a detector assembly 22 having a detector coil 24for generating a time-domain signal related to low-frequencyelectromagnetic radiation produced by the material in the sample.Although not shown in FIG. 1, the sample is placed in a sample region 26adjacent the coil. As will be seen below, the sample region may includean open region between sample coils, a sample collector, such as amembrane, or a sample tube through which sample material can be flowed.The sample region, including specific sample holders, is also consideredpart of the detector assembly. FIG. 6A shows a typical time-domainsignal for a 0.25 M NaCl sample recorded with the apparatus of theinvention.

Shown at 27 are signal conditioning components for converting the signalfrom the detector coil to an amplified conditioned signal from whichfrequency components above a selected frequency have been removed. Thesecomponents, which are illustrated in FIG. 3, which also shows at 25 thetarget analog signal generated by the detector assembly. Components 27in the figure include a low-noise amplifier 28 (an AD 620 LNA 7 orequivalent is suitable) that amplifies the signal to a serviceablelevel, e.g., with a gain of 1 to 1000. The amplifier's gain isaddressable by the user as a method for adjusting the sensitivity thesensing device. Also include in components 27 is an analog low-passfilter which functions to condition the signal for A/D conversion bypassing frequencies below 50 kHz, such as a conventional Butterworthanalog filter. This filter adjusts and limits the bandwidth of thereceived signal depending on down stream processing requirements. Thisfilter is addressable by the user and by the interpreter, depending ondata characteristics held in archive. If the primary spectral componentsof an archived signal occur within a narrow bandwidth the interpretercan instruct the filter to narrow the bandwidth, thereby increasing theefficiency of the Fourier processor. The digital filter also may providea notch filter to remove unwanted signals in noisy environments. FIG. 6Bshows the FIG. 6A signal for a 0.25 M NaCl sample after amplificationand band-pass filtering to remove frequency components above 50 kHz.

The output of the low-pass filter is supplied to an analog-to-digitalconverter 34 for converting the amplified filtered analog signal todigital form. A typical A/D converter is 16 bit analog to digitalconverter, such as supplied Analog Devices (Los Angeles, Calif.),although other resolutions are possible, such as 24 bit.

The output of the A/D converter is operably connected to an electroniccomputer 36 in the apparatus for performing a series of signalprocessing steps leading to the identification of spectral componentsthat are diagnostic of the sample material to be identified, as will bedescribed. The computer includes a digital signal processing module 35which designed for high speed signal processing operations as will bedescribed. Also included in the computer is a data-storage device 37,such as a conventional thin-film storage device 37 for storing, for eachof one or more preselected materials including the selected material, adata set containing low-frequency spectral components that are (i) in aselected frequency range between DC to 50 khz, and (ii) characteristicof that material. This data set is generated by recording and processinglow-frequency signals from a selected material under conditions of highmagnetic and electromagnetic shielding, using the low-frequencyrecording device described in PCT application PCT/US03/09544, filed Mar.28, 2003, for “System and Method for Characterizing a Sample byLow-Frequency Spectra,” which is incorporated by reference herein.Further details are provided below in the section “Generating Signals”.

Briefly, the low-frequency recording device produces a data setcontaining a plurality of low-frequency spectral components, typicallyin the range 100 Hz to 6.6 kHz, but including the broader range of dc to50 kHz, that are characteristic of the sample material, e.g., water, anionized salt, a solute material, a undesired contaminant, a biologicalsample material or the like. In general, the more complex the samplematerial, the more complex (the greater the number of characteristicspectral components) the sample material data set will contain. As willbe appreciated from below, the ability to detect a selected samplematerial by the method of the present invention relies on the fact thatdifferent selected materials of interest are characterized by differentspectral “signatures,” that is, different sets of characteristiclow-frequency components, such that at least one, and typically severalof the characteristic spectral components of a material will be uniqueto that material.

The nature of the data sets that are installed on the storage device inthe apparatus will depend on the selected materials one wishes to detectwith the apparatus. Typically, for a mobile field unit, the data setswill include data sets for all material one expects to detect in a filedsetting, include, for example, various contaminants that may be presentin a water sample, or various air-borne sample one may wish to detect,such as atmospheric particulates, air-borne biological particles orchemical components present in the air.

As indicated above, the computer is designed to perform a variety ofsignal processing operations, as indicated in FIG. 3. The firstoperation signal processing is carried out to selectively pass frequencycomponents corresponding to the low-frequency spectral components of thesample material of interest. Thus, this stage performs the function of aplurality of notch filters, each notch passing one of the spectral bandsin the data set for the material of interest. In performing thisoperation, the computer first retrieves from the data storage device, adata set of low-frequency spectral components characteristic of theselected sample material, and records the low-frequency spectralcomponents associated with that material. This data is used in aconventional digital notch and band pass filter 38 to (i) remove (notchfilter) 60 Hz frequency components, and pass those frequencies (bandpass filter) corresponding to spectral frequencies in the retrieved dataset. In general, the digital filter passes selected frequencies betweendirect current and 50 kHz. The pass band filter may consist of an arrayof addressable digital filters capable of providing multiple pass bandswhile also providing a notch filter at power line frequencies and otherknown noise frequencies. The digital filter is addressable by theprocessor and may also be configured to be user addressable.

There may be some compression in the A/D conversion of signals. There isalso a data buffer 39 between the ADC and the Fourier processor fortemporarily storing streaming data from the input. The results of theanalysis (not the signal) will be presented in some form of graphicdisplay and will also be stored in the onboard data archive for laterretrieval.

In the next processing step, the band-pass filtered time-domain signalis cross-correlated at 40 with the spectral components of the retrieveddata set, to generate a frequency-domain spectrum in a frequency rangewithin DC to 50 KHz that contains, as spectral components, thosespectral frequencies present in both the filtered signal and the dataset. That is, the spectrum will evidence, by the presence of absence ofcharacteristic spectral components, the presence of absence of thesample material of interest. The cross-correlation module performs atime series Fast Fourier Transform on the received data using radix-2,radix-4, or a split-radix algorithm. The resulting data isrepresentative of the original signal consisting of spectral amplitudesmeasured in seconds per cycle (time series equivalent to Time,Frequency, and Amplitude). A spectrum for 0.25 M NaCl generated bycross-correlating the FIG. 6B signal with a data set of spectralcomponents for this sample is shown in 6C.

The cross-correlation function may be performed by a monolithicprocessor chip programmed to perform a Fourier correlative analysis ofthe unknown signal compared with Fourier data from a known signal. Theresults of the analysis will be rendered as a percentage spectral fitbetween the unknown signal and the known signal. The ‘fit’ percentagewill correlate to statistical significance and this data is then sent onto an interpreter to be filtered through an algorithm to determine thelikelihood of a positive detection or match.

The processor may be a digital signal processor (“DSP”) or amicroprocessor instructed to perform cross spectral correlations againstarchived data. The processor may act independently, or in conjunctionwith other processors, depending on architecture. This processor mayalso provide instruction to the active digital filters to establishFourier filters as signal preparation for cross spectral correlation.The processor may be part of a larger processing system which includesdata storage and an appropriate user interface with output device(s)(e.g. visual display, speakers for audio, wireless transceiver, etc.).The user interface may provide for user control over the amplifier andfilter modules, ADC and correlator.

Typically, the spectrum generated above will require additionalfiltering, and/or summing and averaging operations to enhance thespectral components and/or evaluate whether the spectral components areabove a defined statistical threshold that permits a reliable measure ofidentification of the selected sample material. In one exemplary method,a cross-correlated sample signal spectrum is compared with across-correlated noise (no sample) signal. A comparator algorithm theadvances incrementally, e.g., in 0.1 Hz intervals across thecross-correlated sample spectrum and the cross-correlated noisespectrum, looking at the correlation value at each frequency point, andsubtracts the noise correlation from the sample correlation at thatpoint, to yield a frequency plot of corrected correlation values. (Ofcourse, greater or lesser frequency intervals may be used depending uponthe application.) These values will be relative to a particular sample,and depend, for example, on the relative amplitude of any noisecomponent.

Alternatively, the spectrum generated from a single reading may besummed and averaged successively with spectra produced from the samesample. That is, a second spectrum produced as above from the samesample is added to the first spectrum, and the two spectrum and summedand averaged. This process is repeated until spectral components above astatistical threshold are observed. These components are then “read” orcompared to determine if they match the characteristic frequencies ofthe selected material. The above operations are carried out as shown at42 in FIG. 3. A summed and averaged spectrum for an NaCl sample is shownin FIG. 6D.

The interpreter may be another monolithic processor chip capable ofstoring and using a simple algorithm to determine an outcome from anevent (or may be a routine performed by the above processor chip orsystem components). The event will be a number reflecting thestatistical significance (as a percentage) of the fit between theunknown and known signals. The algorithm will measure the ‘fit’ againstpreset thresholds to determine the likelihood of a match based on theknown characteristics of the spectral data for a specific molecule. Asan example, one molecule may have exceptionally high spectral energywithin certain frequencies and cycles over time. Another molecule mayhave more uniform spectral energy over those same frequencies and time.Each of these molecules may be produce a statistical significance thatis different but of equal weight. The interpreter compensates for thiswhen there are multiple known molecular datasets in data archive. In itssimplest embodiment, the interpreter is a threshold against which thepercentage fit signal is compared. The threshold may be adjusted basedon various factors (e.g. reduce the threshold if the environmental noiselevel is high).

The data and/or results of the determinations are shown to the user at acomputer display 44. Alternatively, or in addition, the results arestored in a storage device 46, typically one associated with thecomputer. Also as shown in FIG. 1, the computer has a keyboard 48 bywhich the user can specify signal processing parameters, sampleidentification, and so forth.

A typical output will identify the selected sample of interest, anddisplay a table with side-by-side columns listing (i) the spectralcomponents associated with the selected sample, and (ii) thosestatistically meaningful sample components that match those from thedata set. From this, the user will be able to determine whether theselected material is in the sample. The output may also show an averagedspectrum from which the spectral components are derived, to provide theuser with some statistical measure of the reliability of the results.

An apparatus 50 constructed according to a second embodiment of theinvention is illustrated in FIG. 2. The apparatus here differs fromapparatus 20 just described in the detector assembly only, and thusother components of the apparatus are shown by the same referencenumerals as indicated from apparatus 20. The detector assembly in theapparatus, indicated at 52, includes as the detector coil, a Helmholzcoil 54 comprised of a pair of opposing coil elements 56, 58. The twocoils elements define a sample region 60 between the elements throughwhich a sample, such as indicated at 62, can be moved. This embodimentis useful, for example, for detecting samples, such as portable objects,that are being screened or checked. As shown, the coil elements areseparately connected to the signal amplifier in the noise injectioncoils. In operation, the two separate signals are processed the same asabove.

Numerous alternative components are possible. For example, while thedetector coil is a tuned coil designed to operate between direct currentand 50 kHz capable of detecting low frequency electromagnetic emissionspresent in a specific area, a variety of coils can be implementedincluding, but not limited to: Tesla coils, toroidal coils, Helmholtzcoils, slug tuned coils, and measured wavelength and random wire loops.

The next two sections describe alternative detector assemblies, thefirst having a sample tube and a toroidal detector for detectingmaterial in a fluid sample, and the second having a filter samplecollector for detecting a gas-borne sample material.

A. Toroidal Detector

Many commercial and industrial applications require materials to beevaluated in a process stream in noisy electromagnetic environments.Sensing molecular material inside a toroidal transformer provides a highlevel of electromagnetic isolation. Applications for this systeminclude, but are not limited to, real-time chemical analysis of processstreams in volatile, corrosive or thermal environments, testing air forcontaminates in commercial or public environments, and testing watersupplies or other commercially available consumables.

The system provides the ability to move fluids and gasses through atoroidal transformer while using the fluid or gas as a replacement forthe primary emitting element. A gas or fluid is pumped around thecircumference of the toroid between the torus core and induction coil.Gaussian white noise is injected inside the toroid via a noise elementwrapped in a 1:1 ratio with a sample tube that moves the gas or liquidthrough the toroid (although other ratios may be used).

As molecular material moves through the toroid, Gaussian white noise isinjected 30 to 35 dBs above the level of the molecular signal. Themolecular signal sums with the white noise to form a stochastic productthat retains identifying characteristics of the molecular signal. Thesignal is then amplified and processed as described above, to identifymaterial in the sample having a unique low-frequency spectral signature.

The device uses the folded magnetic field of a toroid and providesinductance between the primary and secondary element (sample tube anddetection coil). The confined magnetic field (B) inside the toroidcavity is accurately quantified by the simple equation: B=A/r, where Ais a proportionality constant (torus factor), and r is the radialdistance from the long axis of the cavity.

A toroidal detector assembly constructed in accordance with thisembodiment of the invention is shown in plan view at 64 in FIG. 4. Aglass, plastic, preferably Pyrex tube 66 is disposed about the outercircumference of a toroidal core 72 formed of a ferromagnetic material,such as Ferrite. The tube communicates at its opposite ends with inletand outlet ports 68, 70, respectively, allowing liquid or gas samples tobe pumped through the tube during a detection operation. The dimensionsof the toroid and tubing are dependent on the individual application andare only critical to the end-product design specifications. Further, thetube ring could be positioned in other locations within the coil, suchas within a center of the core 72.

A noise injection coil 76 is disposed about the outer circumference ofthe tube, as indicated. While the coil 76 is shown as one loop, it couldtake the form of coils disposed as the windings 74. The purpose of coil76 is to allow the addition of Gaussian noise supplied from a Gaussiannoise source noise 78 into the sample.

A secondary winding 74 which forms the detector coil in the assembly iswrapped radially around the toroid core, tube 66, and noise coil 76. Thesize and type of the wire and the number of turns around the toroid isdependent on the resonant frequency of the secondary coil. Although notcritical, the configuration should be such as to achieve as high a Qfactor as possible for the transformer. The detector coil is connectedto a signal amplifier for signal conditioning as described above.

In operation, a fluid or a gas is pumped through the transformer in sucha manner as to provide a sustained low velocity flow. Sample materialtransiting through the tubing emit extremely low amplitudeelectromagnetic waves that are detected by the coil. White noise isinjected into the area of the sample via a noise coil so as to mix withthe natural electromagnetic emission from the target material. Gain isthen applied to the noise until the noise is 30 to 35 dbs above thetarget signal. At this point the noise takes on the characteristics ofthe underlying signal, effectively amplifying the molecular signal undera natural condition known as stochastic resonance. The stochasticproduct creates an induced voltage in the secondary coil that is pickedup and amplified by a preamplifier circuit.

The system effectively uses ambient electromagnetic environmental noiseas a stochastic source and applying Fourier analysis to identifystochastic (waveform) features that resemble (waveform) features ofspecific molecules. This also establishes theoretical limits on thefunctional range of the detector.

Note, the scale of the toroid is dependent upon the application. Forexample, a very large detector may be created with a large toroid tothereby sample a large volume of material.

B. Air Detector

Illustrated in FIG. 5 is apparatus 80 having an air-sample detectorassembly intended for the detection and identification of an unknownmaterials entrained in a gaseous form, e.g., air-borne particles. Theassembly includes an air transport tube, e.g., a 1 micron particulatefilter, a detection coil 84, a radio frequency shield 86, and a pump 88for pumping air through the tube and through the filter. The detectorcoil is oriented with its windings parallel to the plane of the filter,and placed directly against the back surface of the filter.

The air tube can be made of any suitable material, although plastics andfiber composite materials may be preferable. The purpose of the air tubeis to allow airborne particulates to move through the device. Aremovable 1 micron electrostatic filter is used to trap airborneparticulates as air moves through the filter. Alternatively, where theapparatus is intended to measure material in molecular gas form, such asNO, SO₂, fluorocholorohydrocarbons, alkyl gases, and the like, thefilter may provide a chemically reactive surface on which the materialmay be adsorbed or absorbed, or with which the material may react.

Where it is necessary to dissolve the selected material of interest in asolvent in order to detect its low-frequency rotational modes, thedetector assembly in the apparatus may alternatively include separatecollector and detection stations. Thus, for example, the apparatus mayinclude a system for collecting material on a filter, and a separatedetection system that includes a reservoir of solvent in which thefilter is placed, allowing trapped material on the filter to releaseinto the solvent, e.g., water or organic solvent. The solution ofsuspended material may then be interrogated with the solution in a Pyrexvessel having a detector-coil winding about the outside of the vessel.

In still another embodiment, the air is drawn through a reservoir ofsolvent, e.g., water or an organic solvent in which the molecular-gasmaterial is dissolved. That is, the air sample is bubbled through thereservoir at a rate effective to trap the selected material of interest.In this embodiment, the detector coil may be wound about the exterior ofthe reservoir, e.g., the exterior of a Pyrex cylindrical vessel throughwhich the air is bubbled. The liquid sample with entrapped gas materialmay also be transferred to a separate detector, e.g., the above toroidaldetector described above.

III. Generating Signals

Details on how certain signals, such as baseline or data set signalsused for comparing with sampled signals will now be described.

Referring to FIG. 7, a processing unit employing aspects of theinvention includes a sample tray 840 that permits a sample 842 to beinserted into, and removed from, a Faraday cage 844 and Helmholtz coil846. A SQUID/gradiometer detector assembly 848 is positioned within acryogenic dewar 850. A flux-locked loop 852 is coupled between theSQUID/gradiometer detector assembly 848 and a SQUID controller 854. TheSQUID controller 854 may be a model iMC-303 iMAG multichannel controllerprovided by Tristan of San Diego.

An analog noise generator 856 provides a noise signal (as noted above)to a phase lock loop 858. The x-axis output of the phase lock loop isprovided to the Helmholtz coil 846, and may be attenuated, such as by 20dB. The y-axis output of the phase lock loop is split by a signalsplitter 860. One portion of the y-axis output is input the noisecancellation coil at the SQUID, which has a separate input for thegradiometer. The other portion of the y-axis signal is inputoscilloscope 862, such as an analog/digital oscilloscope having Fourierfunctions like the Tektronix TDS 3000b. That is, the x-axis output ofthe phase lock loop drives the Helmholz coil, and the y-axis output,which is in inverted form, is split to input the SQUID and theoscilloscope. Thus, the phase lock loop functions as a signal inverter.The oscilloscope trace is used to monitor the analog noise signal, forexample, for determining when a sufficient level of noise for producingnon-stationary spectral components is achieved. An analog tape recorderor recording device 864, coupled to the controller 854, records signalsoutput from the device, and is preferably a wideband (e.g. 50 kHz)recorder. A PC controller 866 may be an MS Windows based PC interfacingwith the controller 854 via, for example, an RS 232 port.

In FIG. 8, a block diagram of another embodiment of the processing unitis shown. A dual phase lock-in amplifier 202 is configured to provide afirst signal (e.g., “x” or noise signal) to coils 726, 728 and a secondsignal (e.g., “y” or noise cancellation signal) to a noise cancellationcoil of a superconducting quantum interference device (SQUID) 206. Theamplifier 202 is configured to lock without an external reference andmay be a Perkins Elmer model 7265 DSP lock-in amplifier. This amplifierworks in a “virtual mode,” where it locks to an initial referencefrequency, and then removes the reference frequency to allow it to runfreely and lock to “noise.”

An analog noise generator 200 is electrically coupled to the amplifier202. The generator 200 is configured to generate or induce an analogwhite Gaussian noise at the coils 726, 728 via the amplifier 202. As anexample, the generator 200 may be a model 1380 manufactured by GeneralRadio.

An impedance transformer 204 is electrically coupled between the SQUID206 and the amplifier 202. The impedance transformer 204 is configuredto provide impedance matching between the SQUID 206 and amplifier 202.

The noise cancellation feature of the SQUID 206 can be turned on or off.When the noise cancellation feature is turned on, the SQUID 206 iscapable of canceling or nullifying the injected noise component from thedetected emissions. To provide the noise cancellation, the first signalto the coils 726, 728 is a noise signal at 20 dB above the molecularelectromagnetic emissions sought to be detected. At this level, theinjected noise takes on the characteristics of the molecularelectromagnetic signal through stochastic resonance. The second signalto the SQUID 206 is a noise cancellation signal at 45 dB and is invertedfrom the first signal at an amplitude sufficient to null the noise atthe SQUID output (e.g., 180 degrees out of phase with respect to thefirst signal).

The SQUID 206 is a low temperature direct element SQUID. As an example,the SQUID 206 may be a model LSQ/20 LTS dC SQUID manufactured by TristanTechnologies, Inc. Alternatively, a high temperature or alternatingcurrent SQUID can be used. Coils 722, 724 (e.g., gradiometer coils) andthe SQUID 206 (collectively referred to as the SQUID/gradiometerdetector assembly) combined has a magnetic field measuring sensitivityof approximately 5 microTesla/√Hz. The induced voltage in the coils 722,724 is detected and amplified by the SQUID 206. The output of the SQUID206 is a voltage approximately in the range of 0.2-0.8 microVolts.

The output of the SQUID 206 is the input to a SQUID controller 208. TheSQUID controller 208 is configured to control the operational state ofthe SQUID 206 and further condition the detected signal. As an example,the SQUID controller 208 may be an iMC-303 iMAG multi-channel SQUIDcontroller manufactured by Tristan Technologies, Inc. A flux-locked loopmay be operatively positioned between the SQUID and the SQUIDcontroller.

The output of the SQUID controller 208 is inputted to an amplifier 210.The amplifier 210 is configured to provide a gain in the range of 0-100dB. A gain of approximately 20 dB is provided when noise cancellationnode is turned on at the SQUID 206. A gain of approximately 50 dB isprovided when the SQUID 206 is providing no noise cancellation.

The amplified signal is inputted to a recorder or storage device 212.The recorder 212 is configured to convert the analog amplified signal toa digital signal and store the digital signal. In one embodiment, therecorder 212 stores 8600 data points per Hz and can handle 2.46Mbits/sec. As an example, the recorder 212 may be a Sony digital audiotape (DAT) recorder. Using a DAT recorder, the raw signals or data setscan be sent to a third party for display or specific processing asdesired. A lowpass filter 214 filters the digitized data set from therecorder 212. The lowpass filter 214 is an analog filter and may be aButterworth filter. The cutoff frequency is at approximately 50 kHz.

A bandpass filter 216 next filters the filtered data sets. The bandpassfilter 216 is configured to be a digital filter with a bandwidth betweenDC to 50 kHz. The bandpass filter 216 can be adjusted for differentbandwidths.

The output of the bandpass filter 216 is the input to a Fouriertransformer processor 218. The Fourier transform processor 218 isconfigured to convert the data set, which is in the time domain, to adata set in the frequency domain. The Fourier transform processor 218performs a Fast Fourier Transform (FFT) type of transform.

The Fourier transformed data sets are the input to a correlation andcomparison processor 220. The output of the recorder 212 is also aninput to the processor 220. The processor 220 is configured to correlatethe data set with previously recorded data sets, determine thresholds,and perform noise cancellation (when no noise cancellation is providedby the SQUID 206). The output of the processor 220 is a final data setrepresentative of the spectrum of the sample's molecular low frequencyelectromagnetic emissions.

A user interface (UI) 222, such as a graphical user interface (GUI), mayalso be connected to at least the filter 216 and the processor 220 tospecify signal processing parameters. The filter 216, processor 218, andthe processor 220 can be implemented as hardware, software, or firmware.For example, the filter 216 and the processor 218 may be implemented inone or more semiconductor chips. The processor 220 may be softwareimplemented in a computing device.

This amplifier works in a “virtual mode,” where it locks to an initialreference frequency, and then removes the reference frequency to allowit to run freely and lock to “noise.” The analog noise generator (whichis produced by General Radio, a truly analog noise generator) requires20 dB and 45-dB attenuation for the Helmholz and noise cancellationcoil, respectively.

The Helmholz coil may have a sweet spot of about one cubic inch with abalance of 1/100 ^(th) of a percent. In an alternative embodiments, theHelmholtz coil may move both vertically, rotationally (about thevertical access), and from a parallel to spread apart in a pie shape. Inone embodiment, the SQUID, gradiometer, and driving transformer(controller) have values of 1.8, 1.5 and 0.3 micro-Henrys, respectively.The Helmholtz coil may have a sensitivity of 0.5 Gauss per amp at thesweet spot.

Approximately 10 to 15 microvolts may be needed for a stochasticresponse. By injecting noise, the system has raised the sensitivity ofthe SQUID device. The SQUID device had a sensitivity of about 5femtotesla without the noise. This system has been able to improve thesensitivity by 25 to 35 dB by injecting noise and using this stochasticresonance response, which amounts to nearly a 1,500% increase.

After receiving and recording signals from the system, a computer, suchas a mainframe computer, supercomputer or high-performance computer doesboth pre and post processing, such by employing the Autosignal softwareproduct by Systat Software of Richmond Calif., for the pre-processing,while Flexpro software product does the post-processing. Flexpro is adata (statistical) analysis software supplied by Dewetron, Inc.

A flow diagram of the signal detection and processing performed by thesystem is shown in FIG. 9. When a sample is of interest, at least foursignal detections or data runs may be performed: a first data run at atime t₁ without the sample, a second data run at a time t₂ with thesample, a third data run at a time t₃ with the sample, and a fourth datarun at a time t₄ without the sample. Performing and collecting data setsfrom more than one data run increases accuracy of the final (e.g.,correlated) data set. In the four data runs, the parameters andconditions of the system are held constant (e.g., temperature, amount ofamplification, position of the coils, the noise signal, etc.).

At a block 300, the appropriate sample (or if it's a first or fourthdata run, no sample), is placed in the system. A given sample, withoutinjected noise, emits electromagnetic emissions in the DC-50 kHz rangeat an amplitude equal to or less than approximately 0.001 microTesla. Tocapture such low emissions, a white Gaussian noise is injected at ablock 301.

At a block 302, the coils 722, 724 detect the induced voltagerepresentative of the sample's emission and the injected noise. Theinduced voltage comprises a continuous stream of voltage values(amplitude and phase) as a function of time for the duration of a datarun. A data run can be 2-20 minutes in length and hence, the data setcorresponding to the data run comprises 2-20 minutes of voltage valuesas a function of time.

At a block 304, the injected noise is cancelled as the induced voltageis being detected. This block is omitted when the noise cancellationfeature of the SQUID 206 is turned off.

At a block 306, the voltage values of the data set are amplified by20-50 dB, depending on whether noise cancellation occurred at the block304. And at a block 308, the amplified data set undergoes analog todigital (A/D) conversion and is stored in the recorder 212. A digitizeddata set can comprise millions of rows of data.

After the acquired data set is stored, at a block 310 a check isperformed to see whether at least four data runs for the sample haveoccurred (e.g., have acquired at least four data sets). If four datasets for a given sample have been obtained, then lowpass filteringoccurs at a block 312. Otherwise, the next data run is initiated (returnto the block 300).

After lowpass filtering (block 312) and bandpass filtering (at a block314) the digitized data sets, the data sets are converted to thefrequency domain at a Fourier transform block 316.

Next, at a block 318, like data sets are correlated with each other ateach data point. For example, the first data set corresponding to thefirst data run (e.g., a baseline or ambient noise data run) and thefourth data set corresponding to the fourth data run (e.g., anothernoise data run) are correlated to each other. If the amplitude value ofthe first data set at a given frequency is the same as the amplitudevalue of the fourth data set at that given frequency, then thecorrelation value or number for that given frequency would be 1.0.Alternatively, the range of correlation values may be set at between0-100. Such correlation or comparison also occurs for the second andthird data runs (e.g., the sample data runs). Because the acquired datasets are stored, they can be accessed at a later time as the remainingdata runs are completed.

When the SQUID 206 provides no noise cancellation, then predeterminedthreshold levels are applied to each correlated data set to eliminatestatistically irrelevant correlation values. A variety of thresholdvalues may be used, depending on the length of the data runs (the longerthe data runs, greater the accuracy of the acquired data) and the likelysimilarity of the sample's actual emission spectrum to other types ofsamples. In addition to the threshold levels, the correlations areaveraged. Use of thresholds and averaging correlation results in theinjected noise component becoming very small in the resulting correlateddata set.

If noise cancellation is provided at the SQUID 206, then the use ofthresholds and averaging correlations are not necessary.

Once the two sample data sets have been refined to a correlated sampledata set and the two noise data sets have been refined to a correlatednoise data set, the correlated noise data set is subtracted from thecorrelated sample data set. The resulting data set is the final data set(e.g., a data set representative of the emission spectrum of the sample)(block 320).

Since there can be 8600 data points per Hz and the final data set canhave data points for a frequency range of DC-50 kHz, the final data setcan comprise several hundred million rows of data. Each row of data caninclude the frequency, amplitude, phase, and a correlation value.

IV. Methods and Applications

This section describes the method of the invention for interrogatingdetecting one or more selected materials in liquid or gaseous sample. Inpracticing the method, a sample containing a selected material ofinterest is placed adjacent a detector coil, e.g., in one of thedetector assemblies noted above. The detector coil then convertslow-frequency electromagnetic emissions in the material, due at least inpart to rotational modes of the material, to an electromagnetictime-domain signal composed of sample source radiation.

The signal is conditioned to convert it to an amplified conditionedsignal from which frequency components above a selected frequency havebeen removed. The filtered, conditioned time-domain signal toselectively pass low-frequency spectral components that are (i) in afrequency range between dc and 50 khz, and (ii) characteristic of theselected material. This is done by retrieving a data set of spectralcomponents that are characteristic of that material, and filtering theconditioned signal to selectively pass, e.g., with a band width of 0.5to 1 Hz, one of more of the frequency components characteristic of thematerial.

The filtered signal is now cross-correlated with a data set oflow-frequency spectral components that are (i) in a frequency rangebetween dc and 50 khz, and (ii) characteristic of a selected material,to produce a frequency-domain spectrum in the frequency range within dcto 50 khz. From this spectrum, it is determined whether thefrequency-domain spectrum contains one or more low-frequency signalcomponents that are characteristic of the selected material, anddiagnostic of the presence or absence of such material in the sample.

Applications of the method for detecting air, water, food, cosmetic, andindustrial samples are noted above, as are applications in screeningluggage or airline passengers for harmful or illegal substances.

From the foregoing, it will be appreciated how various objects andfeatures of the invention are met. The method and apparatus do notrequire activation of sample and do not employ x-radiation or otherpotentially destructive radiation. The invention is applicable to a widerange of materials, with the only requirement that the material be inany environment that allows molecular rotational movement. Further, thesample being tested can be liquid or air, or a person or a persons'effects, for screening purposes.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” Words using the singular or pluralnumber also include the plural or singular number respectively.Additionally, the words “herein,” “above,” “below” and words of similarimport, when used in this application, shall refer to this applicationas a whole and not to any particular portions of this application. Whenthe claims use the word “or” in reference to a list of two or moreitems, that word covers all of the following interpretations of theword: any of the items in the list, all of the items in the list and anycombination of the items in the list.

The above detailed descriptions of embodiments of the invention are notintended to be exhaustive or to limit the invention to the precise formdisclosed above. While specific embodiments of, and examples for, theinvention are described above for illustrative purposes, variousequivalent modifications are possible within the scope of the invention,as those skilled in the relevant art will recognize. For example, whileprocesses or steps are presented in a given order, alternativeembodiments may perform routines having steps in a different order.While these steps are shown in a particular order, in some embodimentsthese steps are re-arranged, and some steps may be deleted, moved,added, subdivided, combined, and/or modified. Each of these steps may beimplemented in a variety of different ways. Also, while these steps areshown as being performed in series, these steps may instead be performedin parallel, or may be performed at different times.

The teachings of the invention provided herein can be applied to othersystems, not necessarily the system described herein. These and otherchanges can be made to the invention in light of the detaileddescription. The elements and acts of the various embodiments describedabove can be combined to provide further embodiments.

All of the above U.S. patents and applications and other references areincorporated herein by reference. Aspects of the invention can bemodified, if necessary, to employ the systems, functions and concepts ofthe various references described above to provide yet furtherembodiments of the invention.

These and other changes can be made to the invention in light of theabove detailed description. In general, the terms used in the followingclaims should not be construed to limit the invention to the specificembodiments disclosed in the specification, unless the above detaileddescription explicitly defines such terms. Accordingly, the actual scopeof the invention encompasses not only the disclosed embodiments, but allequivalent ways of practicing or implementing the invention under theclaims.

While certain aspects of the invention are presented below in certainclaim forms, the inventors contemplate the various aspects of theinvention in any number of claim forms. For example, while only oneaspect of the invention is recited as embodied as a method, otheraspects may likewise be embodied as a method. Accordingly, the inventorsreserve the right to add additional claims after filing the applicationto pursue such additional claim forms for other aspects of theinvention.

1. A method for detecting a selected material in a sample, comprisingholding the sample while shielding the sample from ambient electricaland magnetic energy; introducing noise into the sample to inducestochastic resonance; storing, for each of one or more preselectedmaterials including the selected material, a data set containinglow-frequency spectral components that are of a selected frequency rangeapproximately above DC; wherein the data set contains low-frequencyspectral components that are of a selected frequency approximately below50 kHz, and characteristic of the selected material; producing, adjacentto the sample, a time-domain signal having signal components related tolow-frequency electromagnetic radiation produced by the selectedmaterial in the sample; converting the produced signal to an amplifiedsignal from which frequency components above a selected frequency aresubstantially omitted; retrieving a data set of low-frequency spectralcomponents characteristic of the selected material; filtering theproduced signal to selectively pass low-frequency spectral componentscorresponding to low-frequency spectral components of the retrieved dataset; cross-correlating the produced signal with the retrieved data setof low-frequency spectral components to produce a frequency-domainsignal, wherein the frequency-domain signal has a frequencyapproximately above DC; wherein the frequency-domain signal has afrequency approximately below 50 kHz; determining whether thefrequency-domain signal contains one or more low-frequency signalcomponents that are characteristic of the selected material, anddiagnostic of a presence or absence of the selected material in thesample; and displaying an output associated with whether thefrequency-domain signal contains one or more low-frequency signalcomponents that are characteristic of the selected material.
 2. Themethod of claim 1, for use in detecting a material in a fluid sample,wherein the method further comprises directing the sample through a tubehaving a detector coil wound about the tube in a winding directionsubstantially perpendicular to the direction of sample flow in the tubefor performing the producing, and wherein a toroidal ferrite core hasthe tube disposed about at least a portion of the circumference of thecore, and the detector coil is wound around the tube and core in aradial winding direction.
 3. The method of claim 1, for use in detectinga material in a fluid sample, wherein the method further comprisesdirecting the sample through a tube having a detector coil wound aboutthe tube in a winding direction substantially perpendicular to thedirection of sample flow in the tube for performing the producing. 4.The method of claim 1, wherein introducing noise includes introducingGaussian noise.
 5. The method of claim 1, for use in detecting gaseousor particulate material in a gaseous-stream sample, wherein the methodfurther comprises trapping the selected material as the sample passesthrough a filter.
 6. The method of claim 1, further comprisingidentifying frequencies of low-frequency signal components whosecross-spectral correlations have a selected statistical measure abovebackground spectral noise.
 7. The method of claim 1, further comprising,at least once: receiving an additional frequency-domain signal for agiven sample; adding the additional signal to the frequency domainsignal; and, averaging the added spectra until components in the summedand averaged signal have a selected statistical measure above backgroundnoise.
 8. An apparatus for detecting a selected material in a sample,comprising: means for placing the sample adjacent a detector coil; meansfor introducing noise into the sample to induce stochastic resonance;means for detecting an electromagnetic time-domain signal produced inthe detector coil by the sample; means for processing the time-domainsignal to selectively pass low-frequency spectral components that are ofa frequency approximately above DC; wherein the means for processing thetime-domain signal selectively pass low-frequency spectral componentsthat are of a frequency approximately below 50 kHz, and characteristicof the selected material; means for cross-correlating the time-domainsignal with a data set of low-frequency spectral components that are ofa frequency approximately above DC, and of a frequency approximatelybelow 50 kHz, and characteristic of a selected material, to produce afrequency-domain signal in the frequency range of approximately DC to 50kHz; and means for determining whether the frequency-domain signalcontains one or more low-frequency signal components that arecharacteristic of the selected material, and diagnostic of a presence orabsence of the selected material in the sample.
 9. The apparatus ofclaim 8, for use in detecting a material in a fluid sample, wherein themeans for placing includes means for flowing the sample through a sampletube having sample inlet and outlet ports, and the detector coil iswound about the tube in a winding direction substantially perpendicularto the direction of sample flow in the tube, wherein the sample tube isdisposed adjacent a toroidal ferrite core, the detector coil is woundaround the tube and core in a radial winding direction, and whichfurther includes means for injecting Gaussian noise into the sample forgeneration of the time-domain signal.
 10. The apparatus of claim 8, foruse in detecting a material in a fluid sample, wherein the means forplacing includes means for flowing the sample through a sample tubehaving sample inlet and outlet ports, and the detector coil is woundabout the tube in a winding direction substantially perpendicular to thedirection of sample flow in the tube.
 11. The apparatus of claim 8,wherein the detector coil includes a Helmholz coil having a pair ofopposed coil elements, and the means for placing includes means forplacing the sample between the coil elements.
 12. The apparatus of claim8, for use in detecting gaseous or particulate material in agaseous-stream sample, wherein the means for placing includes means forpassing the sample through a means for filtering effective to trap suchmaterial, as the sample passes through the means for filtering, and thedetector coil has a winding direction substantially parallel to a planeof the means for filtering.
 13. The apparatus of claim 8, wherein themeans for determining includes means for identifying frequencies oflow-frequency signal components in the frequency domain signal whosecross-spectral correlations have a selected statistical measure abovebackground spectral noise.
 14. The apparatus of claim 8, wherein themeans for determining includes means for receiving an additionalfrequency-domain signal for a given sample, means for adding theadditional signal to the frequency domain signal, and means foraveraging the added spectra, and means for repeating the receiving andadding until components in summed and averaged spectra have a selectedstatistical measure above background noise.
 15. A method for detecting aselected material in a sample, comprising: holding the sample adjacent adetector coil, and introducing noise into the sample, to therebygenerate an electromagnetic time-domain signal composed of sample sourceradiation via stochastic resonance; processing the electromagnetictime-domain signal to amplify the electromagnetic time-domain signal,and selectively pass low-frequency spectral components that are in afrequency range of approximately DC to 50 kHz, and characteristic of theselected material; cross-correlating the electromagnetic time-domainsignal with a data set of low-frequency spectral components that are ina frequency range of approximately DC to 50 kHz, and characteristic of aselected material, to produce a frequency-domain signal in the frequencyrange of approximately DC to 50 kHz; and, determining whether thefrequency-domain signal contains one or more low-frequency signalcomponents that are characteristic of the selected material, anddiagnostic of the presence or absence of such material in the sample.16. The method of claim 15, further comprising automatically extractingthe sample from an environment surrounding the system, and wherein thesample is air or gas.
 17. The method of claim 15, further comprisingdetecting an electromagnetic signal from the sample via a toroidaldetector.